The use of prior information in forecast combination
Year: 1990
Type: article
Abstract: Simple averages often, but not always, outperform more sophisticated “optimal” forecast composites. We used Bayesian shrinkage techniques to allow the incorporation of prior information into the estimation of combining weights; the estimated combining weights were coaxed or “shrunken” toward equality but were not forced to be exactly equal. The least-squares and prior (i.e., arithmetic average) weights then emerged as polar cases for the posterior mean; the exact location depended on prior precision, which was estimated from the data. In a simple example involving U.S. GNP forecasts, a large amount of shrinkage was found to be optimal.
Authors: Francis X. Diebold, Peter Pauly
Institutions: University of Pennsylvania, University of Toronto
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,800
OpenAlex ID: W1513789084